Spatiotemporal AED optimization is generalizable

Comparative Analysis of Out-of-Hospital Cardiac Arrest Outcomes in Health Clinics, Nursing Homes, and Public Places: Implications for Optimizing Automated External Defibrillator Strategies

Purpose

Various factors, such as event location and response time, influence the outcomes of out-of-hospital cardiac arrest (OHCA). Very few studies have explored the delivery of basic life support (BLS) to patients having OHCA at health clinics or nursing homes-settings with professional BLS providers. Thus, in this study, we compared prognostic and survival outcomes between health clinics, nursing homes, and other public places (eg, workplaces and sports facilities/recreational areas) to offer insights for optimizing OHCA outcomes.

Patients

This study included adults who had nontraumatic OHCA in Taoyuan City between January 2017 and December 2022.

Methods

We collected data on patient characteristics, emergency medical service parameters, onsite patient management, automated external defibrillator (AED) locations, OHCA prognosis, and survival outcomes. Multivariate analyses were performed to predict survival to discharge (primary outcome) and neurological outcomes at discharge (secondary outcome).

Results

During the study period, the numbers of OHCA events at health clinics, nursing homes, and other public places were 158, 208, and 1986, respectively. The mean age of OHCA in health medical clinics, nursing home and other public places were 63.4, 81.5 and 64.7, respectively (P value<0.001). The proportion of witnessed events, rate of bystander resuscitation, and frequency of AED utilization were the highest for health clinics (53.2% (84/158), 83.4% (132/158), and 13.3% (21/158), respectively, P value<0.001). The average AED-scene distances and response times were the lowest for health clinics (388.8 m and 5.4 min, respectively). In initial shockable rhythm group, the probabilities of survival to discharge at discharge were the highest for health clinics (aOR=1.41, 95% CI=1.04-1.81, P value=0.041)) and lowest for nursing homes (aOR=0.84, 95% CI=0.76-0.93, P value=0.024).

Conclusion

Our research shows that OHCA patients at medical health clinics have higher rates of witnessing and bystander CPR and AED usage than other public places. However, while survival rates for patients with shockable rhythms are slightly better at health clinics, the neurological outcomes are not significantly different. The AED-scene distances are too far to be used effectively.

Spatiotemporal Analysis of Out-of-Hospital Cardiac Arrest Incidence and Survival Outcomes in Korea (2009-2021)

BACKGROUND

Out-of-hospital cardiac arrest is a major public health concern in Korea. Identifying spatiotemporal patterns of out-of-hospital cardiac arrest incidence and survival outcomes is crucial for effective resource allocation and targeted interventions. Thus, this study aimed to investigate the spatiotemporal epidemiology of out-of-hospital cardiac arrest in Korea, with a focus on identifying high-risk areas and populations and examining factors associated with prehospital outcomes.

METHODS

We conducted this population-based observational study using data from the Korean out-of-hospital cardiac arrest registry from January 2009 to December 2021. Using a Bayesian spatiotemporal model based on the Integrated Nested Laplace Approximation, we calculated the standardized incidence ratio and assessed the relative risk to compare the spatial and temporal distributions over time. The primary outcome was out-of-hospital cardiac arrest incidence, and the secondary outcomes included prehospital return of spontaneous circulation, survival to hospital admission and discharge, and good neurological outcomes.

RESULTS

Although the number of cases increased over time, the spatiotemporal analysis exhibited a discernible temporal pattern in the standardized incidence ratio of out-of-hospital cardiac arrest with a gradual decline over time (1.07; 95% credible interval [CrI], 1.04-1.09 in 2009 vs. 1.00; 95% CrI, 0.98-1.03 in 2021). The district-specific risk ratios of survival outcomes were more favorable in the metropolitan and major metropolitan areas. In particular, the neurological outcomes were significantly improved from relative risk 0.35 (0.31-0.39) in 2009 to 1.75 (1.65-1.86) in 2021.

CONCLUSION

This study emphasized the significance of small-area analyses in identifying high-risk regions and populations using spatiotemporal analyses. These findings have implications for public health planning efforts to alleviate the burden of out-of-hospital cardiac arrest in Korea.

Regional variation in accessibility of automated external defibrillators in British Columbia

OBJECTIVES

Bystander-applied Automated External Defibrillators (AED) improve outcomes for out-of-hospital cardiac arrest. AED placement is often driven by private enterprise or non-for-profit agencies, which may result in inequitable access. We sought to compare AED availability between four regions in British Columbia (BC).

METHODS

We identified AEDs (confirmed to be operational) and emergency medical system (EMS)-treated out-of-hospital cardiac arrests (OHCA) from provincial registries. We compared AED availability between BC's four most populous regions. The primary outcome was the total regional weekly accessible AED-hours per 100,000 population. We also examined: AEDs per 100,000 population and per km2, the ratio of AEDs to OHCA, and the distance from each OHCA to the closest AED.

RESULTS

From provincial registries, we included 879 AEDs from BC's four most populous regions, where 9333 EMS-treated OHCA occurred over a 5-year period. The most common AED location types were stores, public community centres, and office buildings. Ten percent of AEDs were accessible for all hours. Weekly accessible AED-hours/100,000 population in the four regions were: 3845, 1734, 1594, and 1299. AEDs/100,000 population ranged from 22 to 48, and AEDs/km2 ranged from 0.0048 to 0.20. The number of OHCAs per AED per year ranged from 1.1 to 2.8. The median OHCA-to-closest AED distance ranged from 503 (IQR 244, 947) to 925 (IQR 455, 1501) metres. The regional mean accessibility of individual AEDs ranged between 59 and 79 h per week.

CONCLUSION

BC's four most populous regions demonstrate substantial variability in AED accessibility. Further benefit could be derived from AEDs if placed in locations accessible all hours. Our data may encourage community planning efforts to use data-based strategies to systematically place AEDs in optimal locations with strategies to maximize accessibility.

Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial

HIGHLIGHTS

This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.

Factors affecting public access defibrillator placement decisions in the United Kingdom: A survey study

Aim

This study aimed to understand current community PAD placement strategies and identify factors which influence PAD placement decision-making in the United Kingdom (UK).

Methods

Individuals, groups and organisations involved in PAD placement in the UK were invited to participate in an online survey collecting demographic information, facilitators and barriers to community PAD placement and information used to decide where a PAD is installed in their experiences. Survey responses were analysed through descriptive statistical analysis and thematic analysis.

Results

There were 106 included responses. Distance from another PAD (66%) and availability of a power source (63%) were most frequently used when respondents are deciding where best to install a PAD and historical occurrence of cardiac arrest (29%) was used the least. Three main themes were identified influencing PAD placement: (i) the relationship between the community and PADs emphasising community engagement to create buy-in; (ii) practical barriers and facilitators to PAD placement including securing consent, powering the cabinet, accessibility, security, funding, and guardianship; and (iii) 'risk assessment' methods to estimate the need for PADs including areas of high footfall, population density and type, areas experiencing health inequalities, areas with delayed ambulance response and current PAD provision.

Conclusion

Decision-makers want to install PADs in locations that maximise impact and benefit to the community, but this can be constrained by numerous social and infrastructural factors. The best location to install a PAD depends on local context; work is required to determine how to overcome barriers to optimal community PAD placement.

Association between location of out-of-hospital cardiac arrest, on-scene socioeconomic status, and accessibility to public automated defibrillators in two large metropolitan areas in Canada and France

AIM

To compare walking access times to automated external defibrillators (AEDs) between area-level quintiles of socioeconomic status (SES) in out-of-hospital cardiac arrest (OHCA) cases occurring in 2 major urban regions of Canada and France.

METHODS

This was an international, multicenter, retrospective cohort study of adult, non-traumatic OHCA cases in the metropolitan Vancouver (Canada) and Rhône County (France) regions that occurred between 2014 and 2018. We calculated area-level SES for each case, using quintiles of country-specific scores (Q5 = most deprived). We identified AED locations from local registries. The primary outcome was the simulated walking time from the OHCA location to the closest AED (continuous and dichotomized by a 3-minute 1-way threshold). We fit multivariate models to analyze the association between OHCA-to-AED walking time and outcomes (Q5 vs others).

RESULTS

A total of 6,187 and 3,239 cases were included from the Metro Vancouver and Rhône County areas, respectively. In Metro Vancouver Q5 areas (vs Q1-Q4), areas, AEDs were farther from (79 % over 400 m from case vs 67 %, p < 0.001) and required longer walking times to (97 % above 3 min vs 91 %, p < 0.001) cases. In Rhône Q5 areas, AEDs were closer than in other areas (43 % over 400 m from case vs 50 %, p = 0.01), yet similarly poorly accessible (85 % above 3 min vs 86 %, p = 0.79). In multivariate models, AED access time ≥ 3 min was associated with decreased odds of survival at hospital discharge in Metro Vancouver (odds ratio 0.41, 95 % CI [0.23-0.74], p = 0.003).

CONCLUSIONS

Accessibility of public AEDs was globally poor in Metro Vancouver and Rhône, and even poorer in Metro Vancouver's socioeconomically deprived areas.

Strategic placement of automated external defibrillators (AEDs) for cardiac arrests in public locations and private residences

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We ranked businesses for their ability to fill gaps in the AED landscape.

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23% of OHCAs in public, and 4% in homes, were within 100 m of an existing AED.

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Many businesses can simultaneously improve coverage of arrests in public and homes.

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Rankings were largely robust to the coverage radius used (100 m, 200 m, and 500 m).

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Even if all 5006 business locations hosted AEDs, large gaps in OHCA coverage remain.

Aim

Methods

Results

Conclusion

Maximum expected survival rate model for public access defibrillator placement

AIM

Mathematical optimization of automated external defibrillator (AED) placement has demonstrated potential to improve survival of out-of-hospital cardiac arrest (OHCA). Existing models mostly aim to improve accessibility based on coverage radius and do not account for detailed impact of delayed defibrillation on survival. We aimed to predict OHCA survival based on time to defibrillation and developed an AED placement model to directly maximize the expected survival rate.

METHODS

We stratified OHCAs occurring in Singapore (2010-2017) based on time to defibrillation and developed a regression model to predict the Utstein survival rate. We then developed a novel AED placement model, the maximum expected survival rate (MESR) model. We compared the performance of MESR with a maximum coverage model developed for Canada that was shown to be generalizable to other settings (Denmark). The survival gain of MESR was assessed through 10-fold cross-validation for placement of 20 to 1000 new AEDs in Singapore. Statistical analysis was performed using χ² and McNemar's tests.

RESULTS

During the study period, 15,345 OHCAs occurred. The power-law approximation with R² of 91.33% performed best among investigated models. It predicted a survival of 54.9% with defibrillation within the first two minutes after collapse that was reduced by more than 60% without defibrillation within the first 4 minutes. MESR outperformed the maximum coverage model with P-value < 0.05 (<0.0001 in 22 of 30 experiments).

CONCLUSION

We developed a novel AED placement model based on the impact of time to defibrillation on OHCA outcomes. Mathematical optimization can improve OHCA survival.

Calculating real-world travel routes instead of straight-line distance in the community response to out-of-hospital cardiac arrest

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In out-of-hospital cardiac arrest, straight-line distance estimates substantially underestimated actual travel distance for bystanders retrieving a nearby public-access AED and for volunteer first-responders travelling to the scene.

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Using real-world travel estimates changed the identity of the nearest public-access AED in more than a quarter of out-of-hospital cardiac arrests.

Background

Using straight-line distance to estimate the proximity of public-access Automated External Defibrillators (AEDs) or volunteer first-responders to potential out-of-hospital cardiac arrests (OHCAs) does not reflect real-world travel distance. The difference between estimates may be an important consideration for bystanders and first-responders responding to OHCAs and may potentially impact patient outcome.

Objectives

To explore how calculating real-world travel routes instead of using straight-line distance estimates might impact the community response to OHCA.

Methods

We mapped 4355 OHCA (01/04/2016-31/03/2017) and 2677 AEDs in London (UK), and 1263 OHCA (18/06/2017-17/06/2018) and 4704 AEDs in East Midlands (UK) using ArcGIS mapping software. We determined the distance from OHCAs to the nearest AED using straight-line estimates and real-world travel routes. We mapped locations of potential OHCAs (London: n = 9065, 20/09/2019-22/03/2020; East Midlands: n = 7637, 20/09/2019-17/03/2020) for which volunteer first-responders were alerted by the GoodSAM mobile-phone app, and calculated response distance using straight-line estimates and real-world travel routes. We created Receiver Operating Characteristic (ROC) curves and calculated the Area Under the Curve (AUC) to determine if travel distance predicted whether or not a responder accepted an alert.

Results

Real-world travel routes to the nearest AED were (median) 219 m longer (623 m vs 406 m) than straight-line estimates in London, and 211 m longer (568 m vs 357 m) in East Midlands. The identity of the nearest AED changed on 26% occasions in both areas when calculating real-world travel routes. GoodSAM responders’ real-world travel routes were (median) 222 m longer (601 m vs 379 m) in London, and 291 m longer (814 m vs 523 m) in East Midlands. AUC statistics for both areas demonstrated that neither straight-line nor real-world travel distance predicted whether or not a responder accepted an alert.

Conclusions

Calculating real-world travel routes increases the estimated travel distance and time for those responding to OHCAs. Calculating straight-line distance may overestimate the benefit of the community response to OHCA.

A review on initiatives for the management of daily medical emergencies prior to the arrival of emergency medical services

Emergency services worldwide face increasing cost pressure that potentially limits their existing resources. In many countries, emergency services also face the issues of staff shortage-creating extra challenges and constraints, especially during crisis times such as the COVID-19 pandemic-as well as long distances to sparsely populated areas resulting in longer response times. To overcome these issues and potentially reduce consequences of daily (medical) emergencies, several countries, such as Sweden, Germany, and the Netherlands, have started initiatives using new types of human resources as well as equipment, which have not been part of the existing emergency systems before. These resources are employed in response to medical emergency cases if they can arrive earlier than emergency medical services (EMS). A good number of studies have investigated the use of these new types of resources in EMS systems, from medical, technical, and logistical perspectives as their study domains. Several review papers in the literature exist that focus on one or several of these new types of resources. However, to the best of our knowledge, no review paper that comprehensively considers all new types of resources in emergency medical response systems exists. We try to fill this gap by presenting a broad literature review of the studies focused on the different new types of resources, which are used prior to the arrival of EMS. Our objective is to present an application-based and methodological overview of these papers, to provide insights to this important field and to bring it to the attention of researchers as well as emergency managers and administrators.

Socioeconomically equitable public defibrillator placement using mathematical optimization

BACKGROUND

Mathematical optimization can be used to place automated external defibrillators (AEDs) in locations that maximize coverage of out-of-hospital cardiac arrests (OHCAs). We sought to determine whether optimization can improve alignment between AED locations and OHCA counts across levels of socioeconomic deprivation.

METHODS

All suspected OHCAs and registered AEDs in Scotland between Jan. 2011 and Sept. 2017 were included and mapped to a corresponding socioeconomic deprivation level using the Scottish Index of Multiple Deprivation (SIMD). We used mathematical optimization to determine optimal locations for placing 10%, 25%, 50%, and 100% additional AEDs, as well as locations for relocating existing AEDs. For each AED placement policy, we examined the impact on AED distribution and OHCA "coverage" (suspected OHCA occurring within 100 m of AED) with respect to SIMD quintiles.

RESULTS

We identified 49,432 suspected OHCAs and 1532 AEDs. The distribution of existing AED locations across SIMD quintiles significantly differed from the distribution of suspected OHCAs (P < 0.001). Optimization-guided AED placement increased coverage of suspected OHCAs compared to existing AED locations (all P < 0.001). Optimization resulted in more AED placements and increased OHCA coverage in areas of greater socioeconomic deprivation, such that resulting distributions across SIMD quintiles matched the shape of the OHCA count distribution. Optimally relocating existing AEDs achieved similar OHCA coverage levels to that of doubling the number of total AEDs.

CONCLUSIONS

Mathematical optimization results in AED locations and suspected OHCA coverage that more closely resembles the suspected OHCA distribution and results in more equitable coverage across levels of socioeconomic deprivation.

Spatiotemporal analysis of out-of-hospital cardiac arrest in the City of Los Angeles, 2011-2019

OBJECTIVE

The goal of this analysis is to spatiotemporally identify and categorize areas in a large urban city according to Out-of-Hospital Cardiac Arrest (OHCA) rates and No Bystander CPR (NBCPR) risk levels.

STUDY AREA AND PARTICIPANTS

The study comprised all cardiac arrests within the administrative geographic boundary of the City of Los Angeles. The final sample included 15,904 cases that were geolocated within 985 census tracts.

MAIN OUTCOMES AND MEASURES

The primary outcome was stratification of census tracts into risk levels of OHCA and NBCPR by observed spatiotemporal patterns.

RESULTS

Of 985 census tracts in the analytical sample, 182 census tracts (18.5%) were identified as having higher risk of OHCA and NBCPR. This assessment resulted in 129 census tracts in Tier 3 (moderate risk), 36 in Tier 2 (moderate-high risk), and 17 in Tier 1 (highest risk). Census tracts in Tiers 2 and 3 had higher amounts incident OHCA, while those in tier 1 had more OHCA events with NBCPR. These areas were largely contiguous and located in the Central and South areas of Los Angeles.

CONCLUSIONS

Using a novel three-tiered neighborhood risk classification tool, specific neighborhoods have been identified in the second largest city in the U.S. with consistently high or accelerating rates of OHCA and low bystander CPR. Further study of bystander response and community-based public health campaigns are needed in these communities.

Strategies on locations of public access defibrillator: A systematic review

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) is a critical condition with poor outcomes. Although the survival rate increases in those who undergo defibrillation, the utility of on-time defibrillation among bystanders remained low. An evaluation of the deployment strategy for public access defibrillators (PADs) is necessary to increase their use and accessibility. This study was to conduct a systematic review for deployment strategies of PADs.

METHODS

Two authors independently searched for articles published before October 2019 from PubMed, Embase, Web of Science, and Cochrane Library. An independent librarian provided the search strategy and assisted the literature research. We included articles that were focused on the main topic, but excluded those which were missing results or that used an unclear definition. The qualitative outcomes were the utility and OHCA coverage of PADs. We performed a qualitative analysis across the studies, but a quantitative analysis was not available due to the studies' heterogeneity in design and variety of outcomes.

RESULTS

We eventually included 15 studies. Three strategies were presented: guidelines-based, grid-based, and landmark-based. The guidelines-based deployment was common fit for OHCA events. The grid-based method increased the use of bystander defibrillation 3-fold, and 30-day survival doubled. The top 3 landmarks in the landmark-based strategy were offices (18.6%), schools (13.3%), and sports facilities (12.9%). Utility of PADs might increase if we optimize PAD location by mathematical modeling and evaluation feedback.

CONCLUSION

Three deployment strategies were presented. Although the optimal method could not be fully identified, a more efficient PAD deployment could benefit the population in terms of OHCA coverage and survival among patients with OHCA.

Effect of Optimized Versus Guidelines-Based Automated External Defibrillator Placement on Out-of-Hospital Cardiac Arrest Coverage: An In Silico Trial

Background

Mathematical optimization of automated external defibrillator (AED) placement may improve AED accessibility and out‐of‐hospital cardiac arrest (OHCA) outcomes compared with American Heart Association (AHA) and European Resuscitation Council (ERC) placement guidelines. We conducted an in silico trial (simulated prospective cohort study) comparing mathematically optimized placements with placements derived from current AHA and ERC guidelines, which recommend placement in locations where OHCAs are usually witnessed.

Methods and Results

We identified all public OHCAs of presumed cardiac cause from 2008 to 2016 in Copenhagen, Denmark. For the control, we computationally simulated placing 24/7‐accessible AEDs at every unique, public, witnessed OHCA location at monthly intervals over the study period. The intervention consisted of an equal number of simulated AEDs placements, deployed monthly, at mathematically optimized locations, using a model that analyzed historical OHCAs before that month. For each approach, we calculated the number of OHCAs in the study period that occurred within a 100‐m route distance based on Copenhagen’s road network of an available AED after it was placed (“OHCA coverage”). Estimated impact on bystander defibrillation and 30‐day survival was calculated by multivariate logistic regression. The control scenario involved 393 AEDs at historical, public, witnessed OHCA locations, covering 15.8% of the 653 public OHCAs from 2008 to 2016. The optimized locations provided significantly higher coverage (24.2%; P<0.001). Estimated bystander defibrillation and 30‐day survival rates increased from 15.6% to 18.2% (P<0.05) and from 32.6% to 34.0% (P<0.05), respectively. As a baseline, the 1573 real AEDs in Copenhagen covered 14.4% of the OHCAs.

Conclusions

Mathematical optimization can significantly improve OHCA coverage and estimated clinical outcomes compared with a guidelines‐based approach to AED placement.

Optimal in-hospital defibrillator placement

AIMS

To determine if mathematical optimization of in-hospital defibrillator placements can reduce in-hospital cardiac arrest-to-defibrillator distance compared to existing defibrillators in a single hospital.

METHODS

We identified treated IHCAs and defibrillator placements in St. Michael's Hospital in Toronto, Canada from Jan. 2013 to Jun. 2017 and mapped them to a 3-D computer model of the hospital. An optimization model identified an equal number of optimal defibrillator locations that minimized the average distance between IHCAs and the closest defibrillator using a 10-fold cross-validation approach. The optimized and existing defibrillator locations were compared in terms of average distance to the out-of-sample IHCAs. We repeated the analysis excluding intensive care units (ICUs), operating theatres (OTs), and the emergency department (ED). We also re-solved the model using fewer defibrillators to determine when the average distance matched the performance of existing defibrillators.

RESULTS

We identified 433 treated IHCAs and 53 defibrillators. Of these, 167 IHCAs and 31 defibrillators were outside of ICUs, OTs, and the ED. Optimal defibrillator placements reduced the average IHCA-to-defibrillator distance from 16.1 m to 2.7 m (relative decrease of 83.0%; P = 0.002) compared to existing defibrillator placements. For non-ICU/OT/ED IHCAs, the average distance was reduced from 24.4 m to 11.9 m (relative decrease of 51.3%; P = 0.002. 8-9 optimized defibrillator locations were sufficient to match the average IHCA-to-defibrillator distance of existing defibrillator placements.

CONCLUSIONS

Optimization-guided placement of in-hospital defibrillators can reduce the distance from an IHCA to the closest defibrillator. Equivalently, optimization can match existing defibrillator performance using far fewer defibrillators.